Evaluating group-based relevance feedback for content-based image retrieval

We have been developing new relevance feedback algorithms for Content-based Image Retrieval (CBIR) that allow the user to achieve more flexible query. In conjunction with the new user interface, called grouporiented user interface, the user’s interest can be expressed with multiple groups of positive and negative image examples. This provides the users with greater flexibility compared with the previous systems where image query is considered as one-class or two-class problems. In this paper, we analyze our new algorithm qualitatively and quantitatively. For comparison with the previous approaches, the systems are tested on both toy problems and real image retrieval tasks. From the result of our experiments, we suggest when and how our algorithm has advantages over the previous methods.

• Type: Conference
• Year: 2003
• Where: ICIP
• Authors: Munehiro Nakazato, Charlie K. Dagli, Thomas S. Huang
• Keywords: Content-based Image Retrieval, ICIP 2003, Image Processing, Image Retrieval, User Interface